class_docstr: Abstract class :class:`Optimiser` for all optimisers.
  
  It must be implemented by all subclasses in this package.
  
  Every :class:`Optimiser` instance can be run for one single step using the
  :py:meth:`run_step` method. The :class:`Optimiser` does not perform the
  evaluation of the objective function but only proposes values from its
  domain. Therefore an evaluation history must be supplied via the
  :py:meth`update` method. The history can be erased and the
  :class:`Optimiser` brought to the initial state via the :py:meth:`reset`
  method.
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docstr: Set the optimiser history.
  
  This method can be used to warm-start an optimiser.
  
  Args:
      history: :obj:`List[HistoryPoint]`. New history which will
          **overwrite** the old one.
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code: def history(self, history: List[HistoryPoint]):
      """Set the optimiser history.
  
      This method can be used to warm-start an optimiser.
  
      Args:
          history: :obj:`List[HistoryPoint]`. New history which will
              **overwrite** the old one.
      """
      self.reset()
      for hp in history:
          self.update(hp.sample, hp.metrics)
  
file_path: hypertunity/hypertunity/optimisation/base.py
imports: import abc
  import math
  from dataclasses import dataclass
  from typing import Any, Dict, List, Sequence
  from hypertunity.domain import Domain, Sample
label_desc: Use image node_id 3 for calling the Optimiser obj's underlying member method code with example usage: obj.history(history) without return types